IoT-Based Remote Control and Monitoring of Agricultural Irrigation Systems Using Automation Protocols

Abstract

The problem here is that in recent years, the topic of efficient water management in agriculture has become even more critical with the existing impact of climate change, population explosions, and freshwater resources reduction. With traditional irrigation systems, a lot of water would be wasted, crop yields would be lower and poor utilization of resources. Here, we propose a remotely controlled and monitored irrigated agricultural system built on an IoT basis and capable of real-time, scalable, and energy-conserving operation with the use of automation protocols, including MQTT, LoRaWAN, and Modbus TCP/IP. The suggested architecture of the system consists of distributed soil moisture and environmental sensors and ESP32-based microcontroller used to collect local data, LoRaWAN connection for low-power long-range communication, and Raspberry Pi-based gateway that links field devices with a cloud platform where analyses and decisions are reached. A predictive irrigation scheduling algorithm is implemented in the cloud layer using evapotranspiration modeling and past soil-moisture trends and, via secure MQTT and Modbus commands, allows the automated activation and deactivation of solenoid valves and pump units. The above performance was measured in field trials of three months on farm size of 5 acres irrigated with drip irrigation where the results indicated a maximum saving of up to 35 percent water as compared to the traditional manual scheduling and still sustaining optimum soil moisture level that would promote health of crops. The system was able to have a packet delivery reliability of 98.6 percent, an average actuation latency time of less than 0.8 seconds in MQTT and independent operation of more than six months powering the nodes using solar. The benefits of adopting heterogeneous communication protocols to allow legacy equipment to interoperate as well as connect to the IoT cloud through the cloud is demonstrated through comparative analysis with respect to the current practices. These findings justify the practicability of the implementation of the proposed system in various agricultural settings, ranging in size, such as those of a smallholder and large-scale agriculture, contributing to sustainable water consumption, lowering operational prices, and output intensity. The study provides a scalable roadmap to precision agriculture and sets the stage towards integrating the AI-driven predictive model, blockchain-assists in transaction security, and edge computing with better resilience in dip-connected networks, rural areas.